Life Cycle Assessment of Biofuels from Algae Hydrothermal

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Life cycle assessment of biofuels from algae hydrothermal liquefaction: The upstream and downstream factors affecting regulatory compliance Elizabeth Connelly, Lisa M. Colosi, Andres F. Clarens, and James H. Lambert Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502100f • Publication Date (Web): 15 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Life cycle assessment of biofuels from algae hydrothermal liquefaction: The upstream and downstream factors affecting regulatory compliance Elizabeth B. Connelly†, Lisa M. Colosi‡*, Andres F. Clarens‡, James H. Lambert† †

Department of Systems and Information Engineering, University of Virginia, 151 Engineer's

Way, P.O. Box 400747, Charlottesville, VA 22904, USA ‡

Department of Civil and Environmental Engineering, University of Virginia, 351 McCormick

Road, P.O. Box 400742, Charlottesville, VA 22904, USA KEYWORDS. algae; hydrothermal liquefaction; Renewable Fuel Standard; carbon dioxide; biogenic carbon credit

ABSTRACT. Life-cycle greenhouse gas (LC-GHG) emissions are a principal metric used by the US Environmental Protection Agency to determine whether a biofuel qualifies under the US EPA Renewable Fuel Standard (RFS2). This paper identifies important factors in the quantification of LC-GHG of algae-derived diesel, jet fuel, and gasoline, using hydrothermal liquefaction (HTL) as a case study for algal biofuels. The results indicate that, under certain conditions, algae biofuels produced using HTL offer over a 50% reduction in LC-GHG

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emissions compared to their petroleum counterparts, and would thus qualify as advanced biofuel and biomass-based biodiesel under RSF2. However, the results are sensitive to several upstream and downstream factors, specifically the CO2 supply chains used in feedstock production and the fuels that are produced using these algal feedstocks, respectively. Upstream processes for the production of algae include supplying gas or liquid phase CO2 to maintain culture viability, which is unlike terrestrial crops. Many commercial CO2 sources, such as natural deposits, do not qualify for the biogenic carbon credit and consequently, the fuels do not qualify under RFS2. When CO2 is a by-product of industrial processes, the fuels may qualify under RSF2. Downstream, the decisions about the fuel produced are also found to significantly impact LCGHG emissions; whereby combustion of aviation biofuels in the atmosphere, specifically the non-CO2 emissions, contributes to the global warming potential (GWP) of the fuels. When the atmospheric, non-CO2 combustion effects are considered, HTL aviation biofuel achieves only 25% reduction in GWP compared to petroleum jet fuel.

1. INTRODUCTION The United States represents a significant share of the global biofuel economy, constituting 48 percent of global biofuels consumption and 46 percent of global biofuels production.1 As a result, the policies and regulations adopted by the United States have influence over the global biofuel industry and resulting GHG emissions, especially where innovative pathways are involved. An emergent pathway of great interest is the conversion of algae biomass to biocrude using hydrothermal liquefaction to produce a suite of drop-in fuels. This and other algae-tobiofuel pathways seem to offer a variety of benefits relative to conventional terrestrial crop biofuels. HTL is being studied extensively because of its importance to the nascent algae


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biofuels industry2–5 and relative benefits compared to other algae biofuel technologies. Currently, however, the performance of this pathway is poorly characterized in the context of existing U.S. regulations. The Renewable Fuels Standard (RFS) program is the primary means by which the EPA assesses the environmental performance of fuel production pathways. It was created under the Energy Policy Act of 2005 and expanded (to become RFS2) under the Energy Independence Security Act (EISA) of 2007. The stated objectives of the EISA include increasing the production of “clean” renewable fuels, and the central tenant of this rule is the application of lifecycle greenhouse gas (LC-GHG) performance threshold standards to ensure improvements in GHG emissions for new fuel pathways relative to the petroleum fuels they replace. Thus, in assessing biofuel pathways, the EPA must consider both upstream processes for biomass cultivation, the entire biofuel supply chain, and downstream factors associated with fuel use and combustion. Algae are an attractive feedstock for biofuels because of their (i) high productivity per acre, (ii) cultivation possible on non-arable land, thus minimizing competition with conventional agriculture and food production, (iii) utilization of waste water or other non-freshwater supply, (iv) potential carbon recycling from industrial emissions, and (v) compatibility for the production of a variety of fuels and valuable co-products.6 Most conversion efforts to date have focused on producing biodiesel from algae by extracting and upgrading algal lipids.7,8 But, this has proven to be problematic because of the extensive dewatering and drying of the algae biomass prior to oil extraction, which has made the algae bioenergy industry largely abandon pathways focused on lipid extraction. In contrast, pathways utilizing hydrothermal liquefaction (HTL), which consists of liquefying whole algae in a high-pressure (up to 2000 psig), high-heat environment (175-


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450°C), are attractive because they do not require drying of the biomass prior to conversion and the process utilizes the entire cell biomass as opposed to the lipid fraction alone.2–5 Barreiro et al.9 offer a review of the tradeoffs between various algae conversion processing conditions. Despite the growing interest in HTL pathways by the industry, the EPA has yet to certify any algae HTL (AHTL) pathways under RFS2. Several important LCA analyses of this pathway have been published in recent years. Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) spreadsheet analysis tool is used by the EPA to aid in the evaluation of LC-GHG emissions for biofuels that are under consideration for certification. Because preliminary modeling of an AHTL pathway using GREET suggested that the resulting fuels could offer more than 50% reduction in LC-GHG emissions relative to petroleum fuels,4 we sought to confirm these optimistic findings with particular consideration of upstream and downstream burdens. Other lifecycle studies7,10–12 report varying LC-GHG results for similar pathways, in part due to differing modeling assumptions (e.g., feedstock productivity, processing conditions, and finished product(s), etc.). These disparities make it difficult to compare the results at face value and to determine whether the AHTL pathway could be used to produce qualifying fuels under RSF2. Based on LC-GHG emissions, RFS2 defines four categories of renewable fuels: (i) renewable fuel, (ii) advanced biofuel, (iii) biomass-based diesel, and (iv) cellulosic biofuel. Each year, the EPA sets specified volume targets for each of these categories. “Obligated parties,” including all producers or importers of petroleum fuels, must then produce or obtain the required amount of certified biofuels to meet their “renewable volume obligations” (RVOs). Fuels that have been certified by the EPA generate so-called Renewable Identification Numbers (RINs) corresponding to an appropriate category and are then available for sale to entities possessing RVOs so that they


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can meet their obligations. Due to the nested nature of the renewable fuel classifications, and the unique supply and demand for each, some RINs are more valuable than others. Generally, biofuel producers are incentivized to produce biomass-based diesel as opposed to just advanced biofuel or renewable fuel, because RINs generated under the former definition can be used to meet RVOs under the latter (see the Supporting Information for further discussion of RFS2). For RFS2 to effectively reduce the GHG emissions from transportation fuels, it is important for certification of fuel pathways to be based on an accurate accounting of LC-GHG emissions that include both upstream and downstream factors, to guide business decisions accordingly.

Figure 1. The system boundaries of this life cycle assessment expand on previous AHTL models to explicitly consider the effects of upstream burdens from CO2 sources for algae growth and the downstream burdens associated with fuel combustion, which depend on whether the fuel is combusted in the atmosphere or terrestrially. While there are other sources for CO2 than those listed, this study focuses only on CO2 from ethanol production and natural deposits. When calculating the LC-GHG emissions of a biofuel pathway, the EPA applies carbon credits to biomass feedstocks that sequester existing stocks of ambient CO2 from the atmosphere. That


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is, they make the assumption that the amount of CO2 taken up (sequestered from) the atmosphere during photosynthesis and growth of the feedstock is the same amount of CO2 released to the atmosphere when the fuel is subsequently combusted to release energy. Algae, however, differ from terrestrial feedstocks used for biofuel production because large-scale cultivation requires that concentrated CO2 be fed in to the cultivation ponds.13 Unlike terrestrial crops, which obtain their carbon exclusively from the atmosphere, algae ponds must have it delivered. Therefore, accounting for LC-GHG emissions of algae-based fuels should include any upstream burdens associated with the supply of carbon dioxide. Potential sources of CO2 include industrial activities such as power generation (e.g., coal-fired power plants), natural gas processing, ammonia production, ethanol production, hydrogen production, or extracted CO2 from dedicated wells. The largest source of commercial quantities of CO2 is that extracted from natural underground wells. Of all these sources, only the CO2 from ethanol production is biogenic in nature, though algae cultivation using the other industrial sources does not represent a net addition to atmosphere CO2 stocks, as it would be otherwise vented. Extracting CO2 from underground deposits, however, does increase the level of CO2 in the atmosphere. The choice of which CO2 to use directly impacts the LC-GHG performance of algae biofuels in a manner that is unique compared to conventional terrestrial biofuel feedstocks (e.g., corn), which use ambient CO2 exclusively. This work builds on current AHTL studies by explicitly considering upstream burdens from CO2 supply, as shown in Figure 1. In addition to upstream burdens, downstream decisions affect the life cycle impacts of transportation biofuels derived from algae. There is new evidence that aviation emissions, occurring in the upper troposphere and lower stratosphere, have greater climate change consequences than the same emissions occurring at the ground level.14 While the emissions


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profiles of terrestrial algae biofuels and aviation algae biofuels are similar, it is the climate change impacts that occur from combustion in the atmosphere as compared to at ground-level that cause aviation biofuels to be less advantageous than ground-transportation biofuels relative to their petroleum counterparts. Although jet fuel is not an “obligated” fuel under RFS2, qualified pathways can be used to produce aviation biofuel that generate RINs to meet diesel RVOs. For the case of aviation biofuels, it is important, however, to consider the climate change consequences of atmospheric emissions. Decisions about the production of renewable gasoline and diesel versus jet fuel should be based on the relative global warming potential of each distillate, where global warming potential is more relevant to climate change mitigation than simply GHG emissions. Stratton and colleagues15 developed ratios to scale the climate forcing impacts of non-CO2 combustion emissions for both petroleum and bio-based jet fuels during atmospheric combustion. These ratios can be multiplied by the CO2 combustion emissions in order to calculate the well-to-wake global warming implications of aviation fuel and guide decision-making on the use of biofuels in aviation. With the above background, the purpose of this paper is to use a life-cycle assessment approach to characterize the role AHTL biofuels can play in the United States biofuel economy, with a focus on several upstream and downstream factors and the existing fuels certification framework. First, we calculate the LC-GHG emissions of three co-produced AHTL distillates: diesel, jet fuel, and gasoline. Specific to aviation biofuels, we also account for the global warming potential of non-CO2 combustion emissions in the atmosphere. Based on these results, we examine the degree to which AHTL biofuels are consistent with the RFS2 regulatory framework and climate mitigation efforts. In this way, we extend the current literature on LC-


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GHG emissions of AHTL biofuels4,6,7,10–12 by considering the business case through the lens of RFS2. Finally, we discuss the influence these findings could have on decision-making related to biofuel production and the sustainability of transportation fuels.

2. METHODS We model all stages of fuel and feedstock production and distribution, in addition to the upstream burdens from CO2 supply for algae cultivation and downstream burdens associated with the combustion impacts of each fuel type, as shown in Figure 1. The first three stages of algae HTL (i.e., algae cultivation, harvest and dewatering, and hydrothermal liquefaction) are based on the model by Liu et al.,7 the hydrotreatment and distillation stages are based on petroleum refining processes,16 and the fuel transportation and distribution and combustion stages are consistent with GREET17 modeling. The overall model architecture is based on Liu et al.7 Further details on the lifecycle model can be found in the Supporting Information (SI) document. We incorporate a range of parameter values from earlier work, in an attempt to assemble the best possible representation of what the AHTL fuel pathway will look like. Table 1 describes parameters used for baseline results and the minimum and maximum values used for sensitivity analysis. Three sets of values are used for each parameter (i.e., minimum, maximum, and likeliest value), to define an empirical triangular distribution. Resulting distributions are then incorporated into a spreadsheet-based Monte Carlo model. This model is run using the Crystal Ball add-in for Microsoft Excel. We consider two scenarios related to carbon dioxide (CO2) supply for algae cultivation, one for industrial-sourced CO2 and one for CO2 extracted from natural deposits. These scenarios


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differ from one another based on the amount of energy and GHG emissions associated with supplying CO2. Both are considered to be technically feasible. Middleton et al.18 calculate the burdens associated with CO2 from ethanol production as 0.86 MJ and 0.07 kg CO2e per kg of CO2. These burdens are assigned to CO2 in the first scenario, “Industrial CO2”, and represent the minimum impacts associated with CO2 supply. In keeping with the treatment used by GREET and Frank et al.,19 the carbon from these industrial sources is treated as atmospheric, based on the assumption that in the absence of algae cultivation, the industrial sources would emit the CO2 into the atmosphere. For the second scenario, “Extracted CO2”, we consider CO2 from natural deposits, which represents the most burdensome option of the possible sources at 1.74 MJ and 0.21 kg CO2e per kg CO2.18 Because this CO2 is extracted from underground wells, and it is the sole product from these wells, it should not be considered biogenic. Thus, in the extracted CO2 scenario, we do not give a biogenic carbon credit to the LC-GHG emissions of the AHTL biofuels. Allocation of lifecycle burdens among the three AHTL distillates (i.e., renewable diesel, aviation biofuel, and renewable gasoline) is performed on an energy content basis. Table 2 describes the assumed specific energy for each fuel and resulting allocation factor. GREET20 data is used for the transportation and distribution impacts for each type of fuel. We refer the reader to Table S1 of the Supporting Information document for further details on the energy content and allocation by fractions. Combustion emissions are calculated from the carbon content of each fuel, which is based on the assumptions for percentage weight of carbon in the upgraded biocrude and percentage weight of the upgraded biocrude for each distillate. The focus of this study is the source and fate of carbon in an emergent energy pathway, but in order to understand how this quantification is carried out by the EPA it is necessary to interpret the latest


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understanding of chemistry through a policy lens. We consider two cases for the combustion of aviation biofuel: one in which atmospheric combustion emissions are assumed to be the same as terrestrial combustion emissions, and the other in which CO2 combustion emissions are multiplied by a ratio15 to reflect the climate forcing from non-CO2 combustion emissions in the atmosphere. Results are presented in terms of the functional unit, which is 1 MJ of final fuel product. 3. RESULTS 3.1 Formulating estimates of AHTL LC-GHG emissions based on aggregated published work. To assess the likelihood that AHTL fuels could qualify under RSF2, it was first necessary to establish estimates of the LC-GHG emissions arising from the pathway shown in Figure 1. This was done by incorporating our own LCA model, based on first-principles engineering calculations, with previously published results from related analyses. We anticipated that reasonable consensus among all LCA results would demonstrate the representativeness of existing models for the proposed pathway. Figure 2 summarizes our results and compares them with previous studies. The shaded markers depict the results of our study representing both the scenario with minimum CO2 supply burdens (from ethanol production) and maximum CO2 supply burdens (from natural deposits). In this figure, we do not include the combustion stage, which is mathematically equivalent to applying a biogenic carbon credit because this is consistent with methods used by others4,7,10,11,17; under this approach the biogenic carbon credit is assumed to essentially cancel out the combustion emissions. Applying this assumption makes it possible for us to benchmark our results against previous studies, because those also either exclude combustion or apply a biogenic carbon credit. The error bars for our results represent the


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minimum and maximum GHG emissions achieved across scenarios when performing sensitivity analysis with the input parameters shown in Table 1. More details on the benchmarking and sensitivity analysis are presented in Section 3 and 4 of the Supporting Information document.

Figure 2. LC-GHG emissions benchmarked with previous studies for: a) AHTL jet fuel, b) AHTL gasoline, and c) AHTL diesel. Results do not include combustion emissions, such that different carbon accounting methods for combustion emissions do not influence comparisons. The icons represent results from different scenarios presented in previously published studies, as identified in the legend. The error bars for our study depict the results of sensitivity analysis, as presented in Section S4. Regarding Figure 2, it should be noted that there is more published literature on AHTL diesel than other fuels, presumably because early interest in algae-derived biofuels focused almost exclusively on extraction and transesterification of lipids into biodiesel. Benchmarking our diesel results against previously published studies offers the best insurance that our model produces results that are comparable to previous findings. Our “Industrial CO2” supply scenario


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result in LC-GHG emissions within the ranges presented in Liu et al.2 Our AHTL jet fuel results are also similar to four of the six scenarios presented by Fortier and colleagues10. Major design differences (e.g., using wastewater as the source of nutrients for algae cultivation) are expected to be a major factor in the discrepancies. Overall, we believe that our model appropriately represents the LC-GHG emissions based on comparisons with others in the literature, though our estimates are less optimistic. Based on our model, we forecast how the various AHTL fuels compare fare under the RSF2 framework. 3.2 Evaluating LC-GHG estimates against the RFS2 framework Fuels certification under RFS2, whereby RIN values are generated and assigned to the qualifying fuels produced, is based on the percent reduction in LC-GHG emissions achieved by biofuels compared to their petroleum-based alternatives. Figure 3 and Table 3 describe the LCGHG emissions arising from AHTL biofuels relative to that of their petroleum counterparts. When the CO2 used for algae cultivation comes from natural deposits, as in the “extracted CO2” scenario, the resulting AHTL biofuels have higher LC-GHG emissions than petroleum fuels because they are not eligible for a biogenic carbon credit. Thus, none of the fuels produced under this scenario could qualify under RFS2. The LC-GHG emissions calculated under the “industrial CO2” scenario represent a slightly more than 50% reduction in emissions relative to their petroleum counterparts. While CO2 from ethanol production was used to represent industrial CO2, Middleton et al.18 calculate comparable upstream burdens for industrial CO2 from ammonia and hydrogen production and natural gas processing. CO2 as a by-product of power generation could serve as another industrial source, but would increase the LC-GHG emissions by about 60% based on the burdens described reported in Middleton et al.18


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RFS2 requires petroleum fuel producers and importers to meet renewable volume obligations as a defined percentage of gasoline and diesel sales. Although jet fuel is not an obligated fuel under RFS2, aviation biofuel is eligible for RIN generation similar to diesel fuels; therefore, it is of interest to anticipate what RIN categorization AHTL aviation biofuel might achieve. Our results indicate that this depends strongly on assumptions related to combustion of the fuel during the use phase, since there is emerging evidence that atmospheric combustion of a biobased fuel (e.g., as during flight) results in a different emissions profile than terrestrial combustion. This consideration is unique to aviation biofuels. Previously published work by Stratton and coworkers15, indicates that non-CO2 emissions arising during atmospheric combustion, including NOx, soot, sulfate, and water vapor emissions and contrail formations, contribute to overall GWP impacts. These contributions can be accounted for using so-called combustion multipliers for each type of emission. When the non-CO2 atmospheric impacts are neglected, AHTL jet fuel qualifies as biomass-based diesel. In contrast, when the atmospheric combustion multipliers developed by Stratton et al.15 are applied to both aviation biofuel and petroleum jet fuel, the AHTL aviation fuel offers a 25% reduction relative to conventional jet fuel. Therefore, if the EPA chose to consider global warming potential (GWP) in place of LCGHG emissions for jet fuels, AHTL jet fuel would qualify as a renewable fuel. From a biofuel producer’s viewpoint, this is a less desirable designation than those for AHTL biodiesel (biomass-based diesel) or AHTL-derived gasoline (advanced biofuel); however, given the rapidly growing demand for domestic, bio-based fuels, it is encouraging that aviation biofuels produced from algae would qualify under the current national regulatory framework.


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Figure 3. The global warming potentials (GWP) of AHTL biofuels and petroleum-based fuels are presented to compare climate change impacts due to lifecycle of each. Jet fuels are presented “with multiplier” and without the non-CO2 atmospheric combustion emissions included in the GWP. The industrial CO2 scenario is based on CO2 from ethanol production. Extracted CO2 is from natural, underground deposits. The error bars describe the results of sensitivity analysis. 3.3 Model sensitivity to input parameters In evaluating petitions for certification of renewable fuels under RSF2, the EPA considers the probabilistic uncertainty of LCA results, as characterized using an empirical distribution; rather than just the average or expected value. Respecting this, we evaluate the range of LC-GHG emissions estimates from our AHTL modeling results for each type of fuel. Figure 4 is modeled after the EPA “Fuel Pathway Determination under the RFS2”21, showing the results of our sensitivity analysis with respect to AHTL aviation biofuel. Interestingly, all types of fuel share the same general distribution shape when jet fuel calculations exclude atmospheric combustion multipliers. Figure 4a presents the distribution of results for AHTL jet fuel, which is representative of the distributions for other AHTL fuels (see Section 4 of the SI for other


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distributions). All distributions summarize results from 100,000 trials of Monte Carlo simulations on 36 input variables as described in detail in Section 1.2 of the SI.

Figure 4. Cumulative probability density distributions of LC-GHG emissions results for AHTL aviation biofuel calculated (a) without and (b) with the non-CO2 atmospheric combustion multiplier. Without the multiplier, eighty-eight percent of the 100,000 trials resulted in LC-GHG emissions below the 50% reduction threshold for biomass-based diesel RFS2 qualification. With the multiplier, sixty-one percent of the trials resulted in LC-GHG emissions below the 20% reduction threshold for renewable fuel RFS2 qualification. Considering the promising baseline results (those calculated with the “baseline” parameter values), with respect to RFS2 qualification, we need to examine the probability distributions for


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AHTL fuel produced from industrial CO2. Eighty-eight percent of the recorded LC-GHG results (i.e., 88% of the simulation output) for AHTL diesel and gasoline fall below the 50% reduction thresholds for their respective petroleum counterparts; therefore, both fuels are likely to be approved under RFS2 as biomass-based diesel and advanced biofuel, respectively. We thus found that the uncertainty in the parameters did not impact our ability to draw conclusions with respect to the likelihood of certification under RFS2. With more 80% of the Monte Carlo results indicating that AHTL fuels will be certifiable at the most stringent level under RSF2, our results are overwhelmingly encouraging. If atmospheric combustion is considered in determining the GWP of aviation biofuels, the qualification petition for AHTL jet fuel would likely be as a renewable fuel, since the median LC-GHG emissions are lower than that needed to meet other thresholds (i.e., 25% reduction could only qualify as renewable fuel). Based on our sensitivity analysis, about 10% of the forecasted GWP is equal to or greater than petroleum jet fuel, and only 61% of the simulations result in GWP that meet the 20% reduction threshold for renewable fuels. Thus, it is unlikely that the EPA would grant approval under RFS2. In this scenario, the use of biofuels in aviation would be discouraged, and the market might be better served by reserving AHTL fuels for ground transportation, using other fuels for aviation. By considering the relative merit of biofuel replacements to petroleum fuels (percentage LC-GHG emissions reductions), RFS2 would encourage the use of petroleum reserves for atmospheric combustion because terrestrial biofuels offer greater global warming abatement. This is an interesting, unexpected conclusion of this analysis, given the growing demand for aviation biofuels to meet commercial and defense demand.


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Beyond this, sensitivity analysis reveals what are the most influential parameters on LC-GHG emissions for production of AHTL biofuels. Identifying the most influential variables reveals where reducing uncertainty in parameters can enable LCA to best represent reality. That is to say, if the assumed ranges of values for those parameters shown in Figure 5 do not capture the uncertainty in the field, actual LC-GHG emissions could be significantly impacted. Three of the six most influential parameters are related to the HTL process itself, while the remaining three pertain to nutrient cycling (See SI for more details). Regarding the HTL conversion, the most optimistic and pessimistic values for “percent yield of biocrude” (as described in Table 1) mediates corresponding changes in LC-GHG emissions of -19% and +46%. Figure 5a shows the level of LC-GHG emissions corresponding to a 50% reduction compared to petroleum jet fuel and reveals that a sufficiently low biocrude yield results in LC-GHG that do not meet this threshold. Similarly, Figure 5b reveals that, when considering the influence of a single parameter, only when the non-CO2 multiplier is sufficiently low could AHTL jet fuel offer 50% reduction compared to petroleum jet fuel with atmospheric combustion effects included. Additionally maximum and minimum assumed changes in “nutrient recycle efficiency”, “nitrogen [content] in the biomass”, or “upgraded biocrude yield”, mediate moderate to significant changes (at least 5% change) in overall lifecycle LC-GHG emissions. These and similar calculations for sensitivity analysis can be found in Section 4.3 of the SI. These results are consistent with previously published LCA literature pertaining to algae cultivation, in which it has been repeatedly shown that nutrient recycling and/or nitrogen supply with low upstream burdens is important for reducing the LC-GHG emissions. Still, it is interesting and novel that the cultivation-phase nutrient burdens still tend to dominate the overall lifecycle impacts even when an energy-intensive conversion process, such as HTL, is used to produce fuel from the


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biomass. Thus, facility designs that use municipal waste water as the nutrient source for algae cultivation could improve the LC-GHG emissions of AHTL biofuels by offering a supply of nitrogen that is less environmentally burdensome than chemical fertilizers.

Figure 5. Most influential parameters (those resulting in changes greater than +/- 5% of baseline) to the GWP results of AHTL jet fuel produced using CO2 from ethanol production (a) without including non-CO2 combustion emissions, and (b) with non-CO2 combustion emissions included. Results for AHTL diesel and gasoline are similar to (a) jet fuel without the non-CO2 atmospheric combustion effects.

4. DISCUSSION 4.1 CO2 procurement and biogenic carbon credits In the United States, over 70% of supplied carbon dioxide is from natural wells.18 This fact is often overlooked when LCA is used to assess the GHG-emissions associated with algae production. Based on a review of the current algae biofuel literature by Collet et al.22, most studies consider the carbon fixation and combustion emissions to be approximately equal to each


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other. Our results contribute to the existing literature by revealing the importance in these upstream sources of carbon dioxide. If the CO2 used for algae cultivation comes from such wells, the combustion of algal biofuels mediates a net addition of GHG emissions into the atmosphere that cannot be canceled out by photosynthetic uptake. This is because the CO2 is brought to the surface strictly for purposes of producing fuel, rather than being recycled from the existing CO2 in the atmosphere. According to our model, the LC-GHG emissions of AHTL biofuels, under this scenario, would exceed those of petroleum fuels, and consequently would not be eligible for RIN generation under RFS2. The EPA, following the GREET methodology, grants biogenic carbon credits within the existing, certified pathways by which algal lipids are extracted and converted into usable fuel, assuming the CO2 for cultivation is from power plant flue gas.19 The rationale for this is that the flue gas would be vented to the atmosphere if the algae pond were not in place; therefore, use of flue gas CO2 is considered equivalent to using atmospheric CO2. Similarly, algae cultivated on CO2 from other industrial processes, such as ethanol production, earns carbon credits. Additionally, the marginal burdens of capturing CO2 are less than the credit for most processes. Our model predicts that AHTL biofuels, where CO2 is supplied from an industrial source, will offer about 55% reduction in LC-GHG emissions compared to petroleum fuels. New pathway petitions for qualification of algae-derived fuels under RFS2 should require explicit information on the source of CO2. This is currently not required, even though the source of CO2 significantly impacts LC-GHG emissions of AHTL biofuels. If the source is not considered, biogenic carbon credit will likely be given even in cases when it is not deserved. Producers of algal biofuels then have incentive to use the cheapest source of CO2, currently that


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from natural deposits, without facing regulatory consequences, i.e., disqualification for RIN generation. This violates the original intent of the RSF2 framework.

4.2 Atmospheric non-CO2 combustion multipliers In our model, the non-CO2 atmospheric combustion multiplier is the most influential parameter in determining the GWP of AHTL aviation biofuel, as shown in Figure 5b. The value of the nonCO2 combustion multiplier for aviation biofuels can range from 0.6-3.8. Values less than 1.0 indicate that atmosphere combustion generates less GWP than terrestrial combustion. Values greater than 1.0 indicate the opposite, whereby combustion in the atmosphere mediates greater climate changes impacts than combustion at ground level. The latter impacts arise mostly from climate forcing impacts of aircraft induced cloudiness (AIC).15 Thus, it is important to understand how changes in fuel composition lead to contrail and AIC formation in order to calculate the complete climate change implication of using aviation biofuels. Future work characterizing the combustion emissions and the corresponding climate forcing will help us better quantify the merit of aviation biofuels. The multiplier effect suggested by Stratton et al.15 for evaluating fuel combustion in the atmosphere, as opposed to at ground-level, highlights the importance of the marginal benefits from substituting a given biofuel for its petroleum counterpart. Specifically, AHTL biofuel producers should choose to maximize their output of ground transportation fuels instead of aviation biofuel, because the marginal decrease in LC-GHG is greater for ground transport fuels than it is for aviation fuel. For example, assuming AHTL diesel offers a 55% reduction in LCGHG emissions while AHTL jet fuel only offers a 25% reduction (factoring in the non-CO2 multiplier), relative to conventional petroleum counterparts, the best use of limited quantities of


20

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Energy & Fuels

AHTL biofuels would be to replace diesel fuels. Regulations such as the RFS2 can incentivize biofuel producers to make decisions along these lines. For the time being, however, it is expected that the EPA will evaluate aviation biofuels based only on LC-GHG emissions, without considering effects of non-CO2 atmospheric combustion.

4.3 Influence of regulations on decision making The RFS2 regulatory framework not only mandates volumes of renewable fuels that must be produced in the United States, but through the sale of RINs provides economic incentives for biofuel producers whose fuels qualify as renewable. In this way, the RFS2 influences the commercialization potential of various biofuel pathways. AHTL results in the production of multiple biofuel products, where producers will be able to tailor the process to optimize the output of certain fuels. For this reason, RFS2 qualification of each fuel type will influence the business operations of these producers. Because algal fuels cannot meet the definition of cellulosic biofuel, only the other RFS2 categorizations are relevant to AHTL biofuel producers. Of these, biomass-based diesel is the most attractive for algae biofuel producers because these RINs are able to meet the most RVOs and can sell for the highest price (see Tables S5-S6). Renewable gasoline from AHTL can qualify as advanced biofuel, which is nearly as attractive as diesel in terms of price. Because jet fuel can qualify as biomass-based diesel, AHTL diesel and jet fuel are equally attractive to producers when non-CO2 atmospheric combustion emissions are neglected. Our results indicate that if non-CO2 emissions are considered, AHTL jet fuel could qualify only as renewable fuel. In this case, it is likely that AHTL biofuel producers would choose to sell the jet fuel as diesel (because it can be used in diesel engines23) in order to earn higher revenue through RIN sales.


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5. CONCLUSIONS The upstream and downstream factors in the AHTL biofuel life cycle are critical for assessing the relative LC-GHG performance of the fuels, especially in the context of RFS2. The aggregate results show significant reductions in LC-GHG intensity for several types of AHTL fuels, which suggest that these fuels would likely be eligible for certification under RFS2. This information indicates that AHTL diesel and jet fuel can generate RINs under the biomass-based diesel category, while ATHL gasoline can generate advanced biofuel RINs because the fuels exceed the 50% reduction (compared to petroleum fuels) threshold. Biogenic carbon credit, however, is necessary for renewable fuel qualification. Thus, only AHTL biofuels produced from industrial sourced CO2, as opposed to that extracted from natural deposits, should be certified pathways. Our sensitivity analysis reveals that certification of an AHTL pathway should also require consideration another element – namely, application of atmospheric combustion multipliers. If one of the objectives of RFS2, though it is not explicitly expressed in the regulation, is to mitigate climate change, climate forcing from the atmospheric combustion of aviation fuels should be accounted for. As the regulation stands, AHTL jet fuel could generate biomass-based diesel RINs, but when GWP as a whole is considered AHTL jet fuel offers only a 25% reduction in GWP compared to petroleum jet fuel, meaning it could at best generate renewable fuel RINs. When uncertainty is taken into account, AHTL jet fuel would likely be disqualified as a renewable fuel and instead would likely be sold as diesel. From a climate change perspective, this replacement of ground transportation fuels, as opposed to aviation fuels, with renewable fuels would be preferable considering the effects of non-CO2 combustion emissions.


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Energy & Fuels

Finally, the results suggest how AHTL biofuels can contribute to the U.S. biofuel economy. When AHTL fuels use industrial CO2, the performance of the fuels in terms of LC-GHG reveals that these fuels are comparable to biofuel alternatives. The regulatory framework of RFS2 should be made less ambiguous in order to ensure that algal biofuel pathways offer reductions in GWP compared to petroleum fuels by explicitly considering CO2 source and atmospheric combustion effects.


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TABLES Table 1. Parameter inputs for LCA models of algal hydrothermal liquefaction (AHTL) fuels. Variable

Base

Min

Max

Sources

Productivity (g/m2-day)

25

2

50

Frank et al.4; Delrue et al.11; Sills et al.24

Nitrogen in biomass (%wt)

7.8

4.8

9.8

Frank et al.4; Davis et al.12; Jones et al.25

Phosphorus in biomass (%wt)

1.09

0.58

1.6

Liu et al.7; Davis et al.12; Jones et al.25

Carbon in biomass (%wt)

52

41

55

Liu et al.7; Frank et al.4; Davis et al.12; Jones et al.25

Ash content (%wt)

29

5

50

Frank et al.4; Fortier et al.10; Davis et al.12

Nutrient recycle efficiency (%)

60

30

90

Liu et al.7; Delrue et al.11; Davis et al.12

P2O5 energy demand (MJ/kg)

12.72

12.72

15.8

Liu et al.7; GREET20

P2O5 GHG emissions (kg/kg)

0.933

0.9

0.933


NH3 energy demand (MJ/kg)

42.97

42.97

43.20


NH3 GHG emissions (kg/kg)

2.68

2.09

2.68


Biocrude yield (%wt)

41

21

61

Fortier et al.10; Davis et al.12; GREET20

Carbon in biocrude (%)

72.1

65

79.2

Liu et al.7; Frank et al.4; Elliott et al.26

Upgraded biocrude yield (%wt)

81

75

90

Fortier et al.10; Davis et al.12; Jones et al.25

Carbon in upgraded biocrude (%wt)

84.75

84.2

85.4

Elliott et al.26

Non-CO2 atmospheric combustion multiplier

2.22

0.6

3.80

Stratton et al.15


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Energy & Fuels

Table 2. Energy-based allocation factor for algae-derived diesel, jet fuel, and gasoline. Allocation factors are calculated using the expected yield of each fuel (16.7% of the energy is contained in other fractions of the biocrude product), consequent mass output, and the LHV of each output fuel stream. Renewable Diesel

Aviation Biofuel

Renewable Gasoline

Yield (%wt)

51

24

10

Mass Output (kg/ha)

14,310

6,734

2,806

Energy Content (LHV, MJ/kg)

44.0

44.1

34.6

Energy-based Allocation Factor (%)

51.2

24.2

7.9

Table 3. Percent reductions in LC-GHG required for certification under RFS2, with appropriate qualification by D code categorization, for AHTL biofuels under industrial and extracted CO2 supply scenarios. Scenario 1

Scenario 2

(Industrial CO2)

(Extracted CO2)

GWP (gCO2e/MJ)

% Reduction

RFS2 D Code

GWP (gCO2e/MJ)

% Reduction

RFS2 D Code

Diesel

43.67

55%

4

127.85

-33%

None

Jet

43.67

53%

4

127.69

-36%

None

Jet (with 129.64 multiplier)

25%

6

213.66

-24%

None

Gasoline

52%

5

147.32

-60%

None

44.01

ASSOCIATED CONTENT


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Supporting Information. Details of parameters and sensitivity analysis are supplied as Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION Corresponding Author *Email address: [email protected]; Tel: +1 434 924 7961

ACKNOWLEDGMENT This work was supported by the National Science Foundation under Grant No. CBET – 1067563. Additional support was provided by the Commonwealth of Virginia Department of Aviation and the Virginia Center for Transportation Innovation and Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. ABBREVIATIONS RFS, Renewable Fuel Standard; EISA, Energy Independence and Security Act; EPA, Environmental Protection Agency; LC-GHG, lifecycle greenhouse gases; RVO, renewable volume obligation; RIN, renewable identification number; LE, lipid extraction; HTL, hydrothermal liquefaction; GREET, Greenhouse gases, Regulated Emissions, and Energy use in Transportation; AHTL, algae hydrothermal liquefaction; LCA, life cycle assessment; GWP, global warming potential; AIC, aircraft induced cloudiness.


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